Computational Social Science and Complex Systems

J. Kertész, R.N. Mantegna, S. Miccichè, János Kertész, Rosario Nunzio Mantegna, Salvatore Miccichè

• Publisher: SAGE Publications Ltd
• Year: 2019
• Print ISBN: 9781643680361
• eBook ISBN: 9781643680378
• Edition: 1
• Topic: Social Sciences
• Subtopic: Social Science Research & Methodology
• Index: Social Sciences

For many years, the development of large-scale quantitative social science was hindered by a lack of data. Traditional methods of data collection like surveys were very useful, but were limited. The situation has of course changed with the development of computing and information communication technology, and we now live in a world of data deluge, where the question has become how to extract important information from the plethora of data that can be accessed. Big Data has made it possible to study societal questions which were once impossible to deal with, but new tools and new multidisciplinary approaches are required. Physicists, together with economists, sociologists, computer scientists, etc. have played an important role in their development. This book presents the 9 lectures delivered at the CCIII Summer Course Computational Social Science and Complex Systems, held as part of the International School of Physics Enrico Fermi in Varenna, Italy, from 16-21 July 2018. The course had the aim of presenting some of the recent developments in the interdisciplinary fields of computational social science and econophysics to PhD students and young researchers, with lectures focused on recent problems investigated in computational social science. Addressing some of the basic questions and many of the subtleties of the emerging field of computational social science, the book will be of interest to students, researchers and advanced research professionals alike.

Table of contents

• Title Page
• Contents
• Preface
• Course group shot
• Virtual social science
• Measuring social and political phenomena on the web
• Science of success: An introduction
• Introduction to market microstructure and heterogeneity of investors
• A primer on statistically validated networks
• Temporal networks: Characterization, motifs and spreading
• Temporal networks of face-to-face interactions
• Introduction to modeling disease spread in space
• Spatio-temporal infrastructure networks
• List of participants